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Electrical Engineering

Enhancing partial discharge pattern recognition via WGAN-GP and inception-resnet-v2

  • 11-12-2024
  • Original Paper
Published in:
Authors
Hai Jin
Longlong Gao
Jidong Pan
Chaoming Zhang
Hongliang Zhang
Hailong Wang
Published in
Electrical Engineering | Issue 6/2025

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Abstract

The article discusses the importance of partial discharge (PD) pattern recognition in power transformers, which is crucial for maintaining the safety and stability of power systems. Traditional methods for PD defect identification often face limitations due to manual feature extraction and data imbalance. This study introduces a novel approach using the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for data augmentation and the Inception-ResNet-v2 network for recognition. The WGAN-GP generates highly realistic PRPD spectra, enhancing the dataset's diversity and quality, while the Inception-ResNet-v2 network demonstrates superior performance in classifying PD defects. The proposed method significantly improves recognition accuracy, addressing the challenges of data scarcity and imbalance in deep learning applications for power systems.
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previous article Optimal frequency modulation of carrier waves and its application to induction motor drive systems
next article Control of a DC–DC buck converter using adaptive neural network
Title
Enhancing partial discharge pattern recognition via WGAN-GP and inception-resnet-v2
Authors
Hai Jin
Longlong Gao
Jidong Pan
Chaoming Zhang
Hongliang Zhang
Hailong Wang
Publication date
11-12-2024
Publisher
Springer Berlin Heidelberg
Published in
Electrical Engineering / Issue 6/2025
Print ISSN: 0948-7921
Electronic ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-024-02889-5
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